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Implementing approaches based on process mining in inter-organizational collaboration environments presents challenges related to the granularity of event logs, the privacy and autonomy of business processes, and the alignment of event data generated in inter-organizational business process (IOBP) execution. Therefore, this paper proposes a complete and modular data-driven approach that implements natural language processing techniques, text similarity, and process mining techniques (discovery and conformance checking) through a set of methods and formal rules that enable analysis of the data contained in the event logs and the intra-organizational process models of the participants in the collaboration, to identify patterns that allow the discovery of the process choreography. The approach enables merging the event logs of the inter-organizational collaboration participants from the identified message interactions, enabling the automatic construction of an IOBP model. The proposed approach was evaluated using four real-life and two artificial event logs. In discovering the choreography process, average values of 0.86, 0.89, and 0.86 were obtained for relationship precision, relation recall, and relationship F-score metrics. In evaluating the quality of the built IOBP models, values of 0.95 and 1.00 were achieved for the precision and recall metrics, respectively. The performance obtained in the different scenarios is encouraging, demonstrating the ability of the approach to discover the process choreography and the construction of business process models in inter-organizational environments. [ABSTRACT FROM AUTHOR]

Business processes have undergone a significant transformation with the advent of the process-oriented view in organizations. The increasing complexity of business processes and the abundance of event data have driven the development and widespread adoption of process mining techniques. However, the size and noise of event logs pose challenges that require careful analysis. The inclusion of different sets of behaviors within the same business process further complicates data representation, highlighting the continued need for innovative solutions in the evolving field of process mining. Trace clustering is emerging as a solution to improve the interpretation of underlying business processes. Trace clustering offers benefits such as mitigating the impact of outliers, providing valuable insights, reducing data dimensionality, and serving as a preprocessing step in robust pipelines. However, designing an appropriate clustering pipeline can be challenging for non-experts due to the complexity of the process and the number of steps involved. For experts, it can be time-consuming and costly, requiring careful consideration of trade-offs. To address the challenge of pipeline creation, the paper proposes a genetic programming solution for trace clustering pipeline synthesis that optimizes a multi-objective function matching clustering and process quality metrics. The solution is applied to real event logs, and the results demonstrate improved performance in downstream tasks through the identification of sub-logs. [ABSTRACT FROM AUTHOR]

Background: To discover pharmacotherapy prescription patterns and their statistical associations with outcomes through a clinical pathway inference framework applied to real-world data. Methods: We apply machine learning steps in our framework using a 2006 to 2020 cohort of veterans with major depressive disorder (MDD). Outpatient antidepressant pharmacy fills, dispensed inpatient antidepressant medications, emergency department visits, self-harm, and all-cause mortality data were extracted from the Department of Veterans Affairs Corporate Data Warehouse. Results: Our MDD cohort consisted of 252,179 individuals. During the study period there were 98,417 emergency department visits, 1,016 cases of self-harm, and 1,507 deaths from all causes. The top ten prescription patterns accounted for 69.3% of the data for individuals starting antidepressants at the fluoxetine equivalent of 20-39 mg. Additionally, we found associations between outcomes and dosage change. Conclusions: For 252,179 Veterans who served in Iraq and Afghanistan with subsequent MDD noted in their electronic medical records, we documented and described the major pharmacotherapy prescription patterns implemented by Veterans Health Administration providers. Ten patterns accounted for almost 70% of the data. Associations between antidepressant usage and outcomes in observational data may be confounded. The low numbers of adverse events, especially those associated with all-cause mortality, make our calculations imprecise. Furthermore, our outcomes are also indications for both disease and treatment. Despite these limitations, we demonstrate the usefulness of our framework in providing operational insight into clinical practice, and our results underscore the need for increased monitoring during critical points of treatment. [ABSTRACT FROM AUTHOR]

The absence of a Standard Operating Procedure (SOP) can lead to many problems in operations within organisations. Process mining techniques can discover process models that reflect the actual behaviour of the process implementations by using event logs extracted from information systems. However, the process models discovered by process mining often have too many variations and deviations when compared to the actual SOPs of the processes. This study attempted to compare three prediction methods in finding a process model from process mining that has the closest properties to the actual SOP. The compared methods are Receiver Operating Characteristics (ROC), the four quality dimensions, and similarity measures for structural and behavioural similarities. For the experiment, we designed a synthetic SOP that served as a ground truth for evaluating the performance of the three prediction methods in this study. We used a synthetic event log extracted from a dummy information system we particularly built for this study to test the methods. This study’s results can be useful, e.g. for auditors to save a lot of time from conducting extensive surveys when SOPs are not readily available. [ABSTRACT FROM AUTHOR]

This paper discusses a methodology to improve the prevention processes of chronic diseases such as diabetes and strokes. The research motivation is to find a new methodological approach to design advanced Diagnostic and Therapeutic Care Pathways (PDTAs) based on the prediction of chronic disease using telemedicine technologies and machine learning (ML) data processing techniques. The aim is to decrease health risk and avoid hospitalizations through prevention. The proposed method defines a Process Mining Organization (PMO) model, managing risks using a PDTA structured to prevent chronic risk. Specifically, the data analysis is focused on stroke risk. First, we applied and compared the Random Forest (RF) and Gradient Boosted Trees (GBT) supervised algorithms to predict stroke risk, and then, the Fuzzy c-Means unsupervised algorithm to cluster information on the predicted results. The application of the proposed approach is able to increase the efficiency of healthcare human resources and drastically decrease care costs. [ABSTRACT FROM AUTHOR]

This study introduces a framework that integrates business analytics into educational decision-making to improve learner engagement and performance in Massive Open Online Courses (MOOCs), focusing on learning environments in English as a Foreign Language (EFL). By examining three specific research questions, this paper delineates patterns in learner engagement, evaluates factors that affect these patterns, and examines the relationship between these factors and educational outcomes. The study provides an empirical analysis that elucidates the connection between learner behaviors and learning outcomes by employing machine learning, process mining, and statistical methods such as hierarchical clustering, process discovery, and the Mann–Kendall test. The analysis determines that learning patterns, characterized as single-phase or multi-phase, repetitive or non-repetitive, and sequential or self-regulated, are more closely associated with the nature of the educational content—such as books, series, or reading levels—than learner characteristics. Furthermore, it has been observed that learners exhibiting self-regulated learning patterns tend to achieve superior academic outcomes. The findings advocate for integrating analytics in educational practices, offer strategic insights for educational enhancements, and propose a new perspective on the connection between learner behavior and educational success. [ABSTRACT FROM AUTHOR]

Generally, these days people live longer but often with increased impairment and disabilities; therefore, they can benefit from assistive technologies. In this paper, we focus on the completion of activities of daily living (ADLs) by such patients, using so-called Smart Homes and Sensor Technology to collect data, and provide a suitable analysis to support the management of these conditions. The activities here are cast as states of a Markov-type process, while changes of state are indicated by sensor activations. This facilitates the extraction of key performance indicators (KPIs) in Smart Homes, e.g., the duration of an important activity, as well as the identification of anomalies in such transitions and durations. The use of semi-Markov models for such a scenario is described, where the state durations are represented by mixed gamma models. This approach is illustrated and evaluated using a publicly available Smart Home dataset comprising an event log of sensor activations, together with an annotated record of the actual activities. Results indicate that the methodology is well-suited to such scenarios. [ABSTRACT FROM AUTHOR]

Using the right tools to support decision-making for managers is very important. Today, using mathematical methods to support managers in making decisions is prevalent. Data envelopment analysis is one of the mathematical methods used in the mentioned field. In recent years, researchers in this field have developed data envelopment analysis methods to a great extent. Many data organizations use to evaluate performance and other purposes are imprecise and uncertain. To solve this problem, one should use methods whose results are reliable. Stochastic, fuzzy-stochastic, fuzzy, robust, etc. models can be mentioned among these methods. Among non-deterministic methods, stochastic models are of particular importance. This article only reviews the existing stochastic methods for dealing with non-deterministic data. We have divided stochastic methods into four categories to understand the subject better. Also, the fit between the models and the data has been reviewed in the articles. The process mining method is suggested to determine the appropriate model for the available data. Finally, the weaknesses of some previous models have been introduced. Suggestions have been made to fix these weaknesses. The current research results provide researchers in this field with appropriate information regarding stochastic data envelopment analysis. [ABSTRACT FROM AUTHOR]

Process variants usually exhibit a high degree of internal heterogeneity, in the sense that the executions of the process differ widely from each other due to contextual factors, human factors, or deliberate business decisions. Understanding differences among process variants helps analysts and managers to make informed decisions as to how to standardise or otherwise improve a business process. Existing process variant mining approaches typically fall short in full supporting semantic process variability mining, especially rarely taking activity behaviour relationships and trace context semantic into consideration. Here, we propose a semantic process variant discovery method, aimed at solving the difficulty of distinguishing similar-but-different behaviours directly from event logs. More specifically, we adapt concepts of benchmark logs and trace context tree to formalise context semantic of event log, to classify benchmark logs into several parts, thereby the clustered trace cohorts are mapped to discover the configurable process variants. In the experimental part, some performance metrics of the proposed method are evaluated and calculated by real-world event logs, supporting the usefulness of the proposed method. The experimental results show that the proposed method is able to distinguish similar-but-different behaviours and is superior to the characteristic trace clustering method using conventional neural networks. [ABSTRACT FROM AUTHOR]

The learning process in online lectures through the Learning Management System (LMS) will produce a learning flow according to the event log. Assessment in a group of parallel classes is expected to produce the same assessment point of view based on the semester lesson plan. However, it does not rule out the implementation of each class to produce unequal fairness. Some of the factors considered to influence the assessment in the classroom include the flow of learning, different lecturers, class composition, time and type of assessment, and student attendance. The implementation of process mining in fairness assessment is used to determine the extent to which the learning flow plays a role in the assessment of ten parallel classes, including international classes. Moreover, a decision tree algorithm will also be applied to determine the root cause of the student assessment analysis based on the causal factors. As a result, there are three variables that have effects on student graduation and assessment, i.e attendance, class and gender. Variable lecturer does not have much impact on the assessment, but has an influence on the learning flow. [ABSTRACT FROM AUTHOR]

The decline in the marketing volume of Rabobank Group ICT is a serious incident as it can hinder the implementation of an increasing number of software releases for business development. The Service Desk Agent records the activities that occur to find out the problems experienced in the form of an event log. Process mining can be used to generate process model visualizations based on event logs to explicitly monitor the business. Fuzzy Miner and Heuristic Miner algorithms can be used to handle complex event logs. In this study, an analysis of the Rabobank Group ICT incident was carried out with process mining using the Fuzzy Miner and Heuristic Miner algorithms. Process mining is done by discovery, conformance, and enhancement. Based on the results of the study, it is known that the division of the work area is not good enough to cause a team to work on a lot of events while there are other teams that only work on one event. Therefore, it is necessary to have a clear and balanced division of domains and workloads so that incidents do not recur. [ABSTRACT FROM AUTHOR]

Through business process modeling, organizations in general have managed to optimize their processes, through a robust and automated process architecture. Similarly, through risk management, companies seek to ensure that the risks that can negatively affect the achievement of the organizations' strategic objectives are identified, analyzed, and adequately responded to. Based on the risk assessment, an organization will need to choose whether to accept the risk, mitigate the risk, or transfer the risk to the same or another process. When carried out effectively, risk management in business processes ensures that the organization's limited resources are prioritized to address in the most efficient way the problems that will affect them the most, and thus their continuity is achieved. and sustainability. This article explores questions associated with What is a business process, BPM? and What are the risks associated with business processes? How does the use of BPM modeling and process mining facilitate risk management at all stages of the process lifecycle? The most important contributions are related to the mapping of cycle risks to a functional and specific framework, and affect the life cycle in its entirety, which were analyzed through process mining. [ABSTRACT FROM AUTHOR]

In this paper, we propose a deep learning-based approach to predict the next event in hospital organizational process models following the guidance of predictive process mining. This method provides value for the planning and allocating of resources since each trace linked to a case shows the consecutive execution of events in a healthcare process. The predictive model is based on a long short-term memory (LSTM) neural network that achieves high accuracy in the training and testing stages. In addition, a framework to implement the LSTM neural network is proposed, comprising stages from the preprocessing of the raw data to selecting the best LSTM model. The effectiveness of the prediction method is evaluated through four real-life event logs that contain historical information on the execution of the processes of patient transfer orders between hospitals, sepsis care cases, billing of medical services, and patient care management. In the test stage, the LSTM model reached values of 0.98, 0.91, 0.85, and 0.81 in the accuracy metric, and in the evaluation of the prediction of the next event using the 10-fold cross-validation technique, values of 0.94, 0.88, 0.84, and 0.81 were obtained for the four previously mentioned event logs. In addition, the performance of the LSTM prediction model was evaluated with the precision, recall, F1-score, and area under the receiver operating characteristic (ROC) curve (AUC) metrics, obtaining high scores very close to 1. The experimental results suggest that the proposed method achieves acceptable measures in predicting the next event regardless of whether an input event or a set of input events is used. [ABSTRACT FROM AUTHOR]

The World Health Organization has estimated that air pollution will be one of the most significant challenges related to the environment in the following years, and air quality monitoring and climate change mitigation actions have been promoted due to the Paris Agreement because of their impact on mortality risk. Thus, generating a methodology that supports experts in making decisions based on exposure data, identifying exposure-related activities, and proposing mitigation scenarios is essential. In this context, the emergence of Interactive Process Mining—a discipline that has progressed in the last years in healthcare—could help to develop a methodology based on human knowledge. For this reason, we propose a new methodology for a sequence-oriented sensitive analysis to identify the best activities and parameters to offer a mitigation policy. This methodology is innovative in the following points: i) we present in this paper the first application of Interactive Process Mining pollution personal exposure mitigation; ii) our solution reduces the computation cost and time of the traditional sensitive analysis; iii) the methodology is human-oriented in the sense that the process should be done with the environmental expert; and iv) our solution has been tested with synthetic data to explore the viability before the move to physical exposure measurements, taking the city of Valencia as the use case, and overcoming the difficulty of performing exposure measurements. This dataset has been generated with a model that considers the city of Valencia's demographic and epidemiological statistics. We have demonstrated that the assessments done using sequence-oriented sensitive analysis can identify target activities. The proposed scenarios can improve the initial KPIs—in the best scenario; we reduce the population exposure by 18% and the relative risk by 12%. Consequently, our proposal could be used with real data in future steps, becoming an innovative point for air pollution mitigation and environmental improvement. [ABSTRACT FROM AUTHOR]

The analysis of business processes based on their observed behavior recorded in event logs can be performed with process mining. This method can discover, monitor, and improve processes in various application domains. However, the process models produced by typical process discovery methods are difficult for humans to understand due to their high complexity (the so-called "spaghetti-like" process models). Moreover, these methods cannot handle uncertainty or perform predictions because of their deterministic nature. Recently, researchers have been developing predictive approaches for running business cases of processes. This paper focuses on developing a predictive business process monitoring approach using reinforcement learning (RL), which has been successful in other contexts but not yet explored in this area. The proposed approach is evaluated in the banking sector through a use case. [ABSTRACT FROM AUTHOR]

Purpose: Process mining connects the disciplines of data mining and machine learning to business process management techniques. A business process is a series of independent and interdependent activities that transform inputs (data, materials, etc.) using one or more resources (such as time, employees, and money). It utilizes the necessary outputs. It is possible to examine the actual behavior of organizations, including the performance of individuals, departments, and resources, using process analysis techniques. The results of the process analysis, which typically includes the organization's business process models, can be compared to the organization's documents and requirements. Thus, processes will be able to be compared, reviewed, monitored, and enhanced. Process mining methods operate based on event logs stored in information systems. Using process mining without high-quality input data will not result in accurate conclusions about an organization's business processes. In recent years, researchers have focused on the evaluation and enhancement of the quality of input data using process mining techniques. The objective of this study is to identify and categorize the most significant data quality issues, as well as recognize the approaches proposed to address this challenge in process mining. Methods: This research employs a systematic review with the intent of analyzing all valid evidence in order to answer the research questions. This study investigates 102 academic studies published between 2007 and 2021, including conference papers, journal articles, and theses. Towards this end, a systematic three-part research methodology was employed. In the first section, which included the research definition, the research field was defined first, followed by the research objectives and queries. In the concluding step of this section, the research's scope is defined. In the second section, the research methodology and entry criteria for the studies discovered during the search for scientific resources are defined. Finally, the identified studies are evaluated in terms of their citations and classified. In the third section, which is devoted to the evaluation of the research, the concluding research of the study is conducted, and then, based on the investigation of the preceding studies, the findings and conclusions are determined. Important data and evidence were extracted from the collated research, allowing for the creation of the necessary tables and graphs. Findings: In recent years, researchers have paid more attention to data quality challenges in the process mining, according to the findings of recent research. In 2019 and 2020, the greatest number of studies will have been published. It was also discovered that the majority of articles were published in three scientific databases, namely Springer, IEEE, and Elsevier. 51% of the studies examined were presented at prestigious conferences. 36% of the studies were published in prestigious scientific journals, while the remaining 13% were represented in dissertations and university reports. The study of the selected articles revealed that 20 data quality issues that can arise in the input data have been investigated in the literature. These challenges have been categorized into five levels: trace, event, case, activity, and timestamps, and four foundational approaches have been identified that have been used to evaluate and resolve data quality challenges in the mining process. 1) data quality frameworks 2) preprocessing 3) anomaly detection 4) repair. Our findings indicate that preprocessing techniques that seek to remove chaotic and infrequent behaviors from the event log have received more attention than other techniques. In addition, these results demonstrate that, in recent years, the discovery of anomalies and the reconstruction of missing events have become popular research topics within the field of process mining. Examining studies related to the field of data quality in the data mining process reveals an abundance of approaches and methods for addressing data quality challenges. Investigations revealed that the use of colorful Petri nets as a mathematical method has been considered in all selected research projects. Conclusions: The data needed for process mining methods can be obtained from various sources. One of the major advantages of process mining is that it is not limited to a specific type of system. Any workflow-based system, such as ticketing, resource management, databases, data warehouses, legacy systems, and even manually collected data, can be analyzed as long as it can be separated using case ID, activity, and timestamp attributes. In real-world scenarios, most data is not collected for process mining purposes or is unsuitable for use in process mining analyses. Especially data that is recorded manually or scattered among various isolated systems can contain errors. Despite the efforts made to improve the quality of input data in the mining process, it is still necessary to develop efficient frameworks and methods to identify, evaluate, and address data quality challenges in real business processes, which are often characterized by high volume and complexity. The results of this research can offer a fresh perspective for researchers, data science specialists, and business analysts. [ABSTRACT FROM AUTHOR]

Process mining is a discipline that analyzes real event data extracted from information systems that support a business process to construct as-is process models and detect performance issues. Process event data are transformed into event logs, where the level of data quality directly impacts the reliability, validity, and usefulness of the derived process insights. The literature offers a taxonomy of preprocessing techniques and papers reporting on solutions for data quality issues in particular scenarios without exploring the relationship between the data quality issues and solutions. This research aims to discover how process mining researchers and practitioners solve certain data quality issues in practice and investigates the nature of the relationship between data quality issues and preprocessing techniques. Therefore, a study was undertaken among prominent process mining researchers and practitioners, gathering information regarding the perceived importance and frequency of data quality issues and solutions and the participants' recommendations on preprocessing technique selection. The results reveal the most important and frequent data quality issues and preprocessing techniques and the gap between their perceived frequency and importance. Consequently, an overview of how researchers and practitioners solve data quality issues is presented, allowing the development of recommendations. [ABSTRACT FROM AUTHOR]

Traditional approaches for process modeling and process analysis tend to focus on one type of object (also referred to as cases or instances), and each event refers to precisely one such object. This simplifies modeling and analysis, e.g., a process model merely describes the lifecycle of one object (e.g., a production order or an insurance claim) in terms of its activities (i.e., event types). However, in reality, there are often multiple objects of different types involved in an event. Think about filling out an electronic form referring to one order, one customer, ten items, three shipments, and one invoice. Object-centric process mining (OCPM) takes a more holistic and more comprehensive approach to process analysis and improvement by considering multiple object types and events that involve any number of objects. This paper introduces object-centric event data (OCED) and shows how these can be used to discover, analyze, and improve the fabric of real-life, highly intertwined processes. This tutorial-style paper presents the basic concepts, object-centric process-mining techniques, examples, and formalizes OCED. Fully embracing object centricity provides organizations with a "three-dimensional" view of their processes, showing how they interact with each other, and where the root causes of performance and compliance problems lie. [ABSTRACT FROM AUTHOR]

The ideology behind process modelling is to visualise lengthy event logs into simple representations interpretable to the end user. Classifying process models as simple or complex is based on criteria that evaluate attributes of models and quantify them on a scale. These metrics measure various characteristics of process models and describe their qualities. Over the years, vast amounts of metrics have been proposed in the community, making it difficult to find and select the appropriate ones for implementation. This paper presents a state-of-the-art meta-review that lists and summarises all the evaluation metrics proposed to date. We have studied the behaviour of the four most widely used metrics in process mining with an experiment. Further, we have used seven healthcare domain datasets of varying natures to analyse the behaviour of these metrics under different threshold conditions. Our work aims to propose and demonstrate the capabilities to use our selected metrics as a standard of measurement for the process mining domain. [ABSTRACT FROM AUTHOR]

This paper provides a novel methodology for human-driven decision support for capacity allocation in labour-intensive manufacturing systems. In such systems (where output depends solely on human labour) it is essential that any changes aimed at improving productivity are informed by the workers' actual working practices, rather than attempting to implement strategies based on an idealised representation of a theoretical production process. This paper reports how worker position data (obtained by localisation sensors) can be used as input to process mining algorithms to generate a data-driven process model to understand how manufacturing tasks are actually performed and how this model can then be used to build a discrete event simulation to investigate the performance of capacity allocation adjustments made to the original working practice observed in the data. The proposed methodology is demonstrated using a real-world dataset generated by a manual assembly line involving six workers performing six manufacturing tasks. It is found that, with small capacity adjustments, one can reduce the completion time by 7% (i.e., without requiring any additional workers), and with an additional worker a 16% reduction in completion time can be achieved by increasing the capacity of the bottleneck tasks which take relatively longer time than others. [ABSTRACT FROM AUTHOR]

Single nucleotide polymorphism (SNP) is one of the most widely used molecular markers to help researchers understand the relationship between phenotypes and genotypes. SNP calling mainly consists of two steps, including read alignment and locus identification based on statistical models, and various software have been developed and applied in this issue. Meanwhile, in our study, very low agreement (<25%) was found among the prediction results generated by different software, which was much less consistent than expected. In order to obtain the optimal protocol of SNP mining in tree species, the algorithm principles of different alignment and SNP mining software were discussed in detail. And the prediction results were further validated based on in silico and experimental methods. In addition, hundreds of validated SNPs were provided along with some practical suggestions on program selection and accuracy improvement were provided, and we wish that these results could lay the foundation for the subsequent analysis of SNP mining. [ABSTRACT FROM AUTHOR]

The wear characteristics and related mechanisms of the Longwall Shearer Haulage System were investigated. Wear is one of the main reasons for failures and downtimes. This knowledge can help solve engineering problems. The research was carried out at a laboratory station and a test stand. The publication presents the results of tribological tests carried out in laboratory conditions. The aim research was to select the alloy intended for casting the toothed segments of the haulage system. The track wheel was made by the forging method using steel 20H2N4A. Haulage System was tested on the ground using a longwall shearer. Selected toothed segments were subjected to tests on this stand. The cooperation of the track wheel and toothed segments in the tootbar were analyzed by a 3D scanner. Debris chemical composition was also appointed, as well as mass loss of toothed segments. The developed solution toothed segment an increase in the service life of the track wheel in real conditions. The results of the research also contribute to reducing the operating costs of the mining process. [ABSTRACT FROM AUTHOR]

Traditional business process-extraction models mainly rely on structured data such as logs, which are difficult to apply to unstructured data such as images and videos, making it impossible to perform process extractions in many data scenarios. Moreover, the generated process model lacks analysis consistency of the process model, resulting in a single understanding of the process model. To solve these two problems, a method of extracting process models from videos and analyzing the consistency of process models is proposed. Video data are widely used to capture the actual performance of business operations and are key sources of business data. Video data preprocessing, action placement and recognition, predetermined models, and conformance verification are all included in a method for extracting a process model from videos and analyzing the consistency between the process model and the predefined model. Finally, the similarity was calculated using graph edit distances and adjacency relationships (GED_NAR). The experimental results showed that the process model mined from the video was better in line with how the business was actually carried out than the process model derived from the noisy process logs. [ABSTRACT FROM AUTHOR]

With the increase of mining depth, disaster degree of rock burst in fault-controlled area is becoming more and more serious. To analyze mechanism and control method of fault rock burst, No. 3316 mining face controlled by No. 3DF56 fault in Jining mine, China, is selected as case study. First, by analysis of general situation of the mining face, mechanical characteristics of coal rock, and micro-seismic events, it is determined that the coal seam has the possibility of rock burst in the process of the face mining towards the fault. Second, based on strength theory of rock, criterion of fault shear instability is obtained, and influence of cohesion and friction angle on the stability of fault is analyzed, and it is concluded that improving the cohesion of rock can control the fault slip. Third, by borehole detection recorder, it is determined that fracture areas of fault surrounding rock distribute in the range of 0–4 m from the surface of roadway surrounding rock and 0–5.0 m from the fault. Finally, control method of "precise grouting to couple shallow and deep boreholes" is introduced, and the grouting borehole scheme is optimized. Field test results show that the method can improve the compressive strength of coal rock and distribution laws of micro-seismic events, weaken the possibility of fault slip, effectively control the deformation of roadway surrounding rock and improve the control effect of rock burst. It can provide a reference for other coal mines with similar geological conditions. [ABSTRACT FROM AUTHOR]

The problem of high stress conditions is a critical challenge for mining activity in deep metal mines, and the difficulty and economic cost of maintaining stope stability also increases. In order to ensure stope stability in the mining process, it is necessary to evaluate stope stability for a more stable stope structure. However, there are many kinds of methods for rock stability assessment, and most of them are based on empirical and analytical methods, which lead to uncertain results. In this paper, a comprehensive evaluation method including factors of stope stability, self-stabilization time, maximum safety span, and support measure is determined, which is more likely to provide a comprehensive result. Finally, it was verified by the field application in Jiaojia Gold Mine. Results show that it can accurately evaluate the stope stability from different aspects compared with the traditional single evaluation method, and it leads to a more comprehensive and reliable result. [ABSTRACT FROM AUTHOR]

Process model resulting from small event log datasets can be easily done because currently available applications are relatively able to do so. However, when faced with event logs from big data, modelling will force the existing applications to work hard. So far, the steps used are sampling of event logs resulting from the system. The problem arising is that the sampling process must be done several times because it has to check the desired fitness value on the sample taken. If the fitness value has not been got, then the sample size is added and the fitness value at a certain iteration is calculated until the required fitness value is met. There are many steps to do with this mechanism. Thus, this paper proposes an alternative way to reduce the steps by using an appropriate sampling technique. The mechanism used is statistical-based sampling simulation in the event log datasets to determine which sampling method is stable in the process modelling. The simulation results show that the sampling method using Cluster Random Sampling with the error rate or Alpha of 1 % has a relatively stable process model and can represent the process model resulting from the event log population. [ABSTRACT FROM AUTHOR]

Based on the field conditions of Huojitu Coal Mine of Shendong coal field in Northern China, eight dynamic numerical models are developed to examine the interaction between the shield and surrounding rock of the longwall panel. The models take into consideration the actual mining process and are developed to simulate the change in the working resistance of the shield for the same shallow burial but with different mining height conditions, ranging from 2.5 to 7 m. The results show that the average shield resistance during the basic roof initial and periodic weighting is 7 to 20 times the weight load of the mining height under the same shallow stratum conditions. By means of dimensional analysis, a relation between mining height and shield resistance in longwall panels is derived theoretically and is fitted by using the shield resistance data under different mining heights monitored in the numerical simulations. This work has led to a new method for the selection of shield support in shallow coal seams. [ABSTRACT FROM AUTHOR]

The regional environmental–geochemical surveying of the long-term impacts of mining and ore processing on a large part of the Zambian Copperbelt mining district was carried out by the Czech Research Group with cooperation of the Geology Department, University of Zambia, and the Geological Survey of Zambia in the period 2002–2018. This included the characterization of various sources of contamination, the extent of contamination of soils and crops, and the degree of contamination of river water and sediments. Solid speciation studies of potentially harmful chemical elements (PHEs), plant and human bioaccessibility studies, and a range of mineralogical techniques were used to assess the pathways of PHE cycling in terrestrial and aqueous systems and their impacts on human health. Ores of the Zambian Copperbelt mining district are mined for Cu and Co, but a number of other trace elements (Pb, As, Cd, Hg, Pb, Zn) gradually accumulated in soils and stream sediments. It was concluded that the most important problems related to ore mining and processing are the contamination of soil and crops due to dust fall out from tailing facilities and emissions from smelters. Moreover, leakages of solutions from tailing dams, insufficient technological control of their stability and breakdowns on pipelines transporting slurry from treatment plants to tailing impoundments cause contamination of water courses and deposition of metal(loids) in stream sediments. However, the contamination of the Kafue River water is relatively limited due to its high neutralization capacity. In contrast, in some Kafue River tributaries, especially those close to big mining centers, the concentrations of dissolved Cu and Co are high (up to 14,752 μg/L and 1917 μg/L) and exceed Zambian effluent limits. We also recommend measures that could contribute to minimizing the impact of ore mining and processing on the environment and the health of the local population. [ABSTRACT FROM AUTHOR]

The consensus protocol is the core of blockchain systems. The existing hybrid committee-based consensus protocol is one of the important ways to improve the permissionless blockchain system. But the presence of malicious nodes, it can interfere with the normal operation of the committee. However, there is a deficiency in the existing mechanism to ensure the efficient and secure operation of the committee. Therefore, a reliable committee consensus protocol (DCCP) for permissionless blockchain is proposed. The protocol guarantees the operation of the committee in three ways. First, a reliability evaluation model is designed based on the performance of committee members. Second, a reliable node selection strategy is proposed by combining the mining process and node reliability value. Third, a malicious node replacement mechanism is proposed to optimize the committee operation. Finally, a delayed reward mechanism is designed in order to encourage node participation. The experiment shows that the DCCP protocol can effectively inhibit the entry of malicious nodes and can improve the throughput by 12% in the presence of malicious nodes, with high throughput and security. [ABSTRACT FROM AUTHOR]

Graph mining is the process of extracting and analyzing patterns from graph data. Graphs are a data structure that consists of a set of nodes and a set of edges that connect these nodes. Graphs are often used to represent real-world entities and the relationships between them. In a graph database, the importance of a pattern (also known as support) must be quantified using a counting function called a support measure. This function must adhere to several constraints, such as antimonotonicity that forbids a pattern to have support bigger than its sub-patterns. These constraints make the tasks of defining and computing support measures highly non-trivial and computationally expensive. In this paper, I use the previously discovered relationship between support measures in graph databases and flows in networks of subgraph appearances to simplify the process of computing support measures. I show that the network of pattern instances may be successfully pruned to contain just particular kinds of patterns and prove that any legitimate computing support measures in graph databases can adopt this strategy. When the suggested method is utilized, experimental evaluation demonstrates that network size reduction is significant. [ABSTRACT FROM AUTHOR]

To study the fracture law of different overlying strata during mining of the lower protective seam under a close distance coal seam at a depth of 1000 m, roof bed‐separation and fissure imaging monitoring were carried out in the inlet roadway ahead of the working face of the JI15‐31040 lower protective seam in the Pingdingshan XII coal mine. The results show that with the overlying strata of the lower protective seam being affected by mining, the displacement of the overlying strata in the roof of 0–4 m was relatively large, and the fissure development was very significant. The displacement of the overlying strata in the roof of 4–12 m was relatively small, the fissures in this range were mostly randomly closed fissures, and this area was stable. Bed separation occurred in the roof of 12–16 m, and the bed‐separating fissures were mostly located at the coal–rock junction. During the mining process, the fractal dimension of the roof gradually increased, and the fissure network developed continuously. The changing process of the fractal dimension of the roof can be roughly divided into three typical change areas: less influence area (distance from the working face > 70 m), rapid increase area (15 m < distance from the working face < 70 m), and stable area (distance from the working face < 15 m), and the area within 70 m in front of the working face was the key supporting area. Under the influence of mining, the roof fissures showed an overall change trend of "from zero to some, from short to long, from small to large, and from narrow to wide." This study can provide a reference for mining of the protective seam in deep coal seams. [ABSTRACT FROM AUTHOR]

There has been a global effort in the past decade, especially in major coal-producing countries, toward understanding the mechanics involved in the stability of coal mine ribs. Buckling and spalling of mine ribs are known to have an impact on their stability and degradation. The generation, propagation, and coalescence of cracks in mine pillar ribs are significantly affected by the overburden depths. In addition, the in situ stress magnitudes tend to affect the rib damage process. High horizontal stresses and increased depths can lead to unfavorable stress conditions, inducing coal mass damage and strength loss. Understanding the dynamics involved in rib behavior will inform better rib control practices. This study intended to assess the effect of mining depth, mining height, and supports on coal mine rib stability. In this research, the response of the coal mass was studied using distinct element modeling to better understand the failure process of coal mine ribs. The study confirmed mining depth as a significant factor controlling the rib loading and failure mechanism. In addition, increased mining heights increased the rib deformation and failure process. The evaluated support effect revealed that at shallower depths, shorter bolt lengths are sufficient to control rib stability. Increasing the bolt length for depths greater than 250 m is in order, but higher depths do not correlate with longer supports. The approach used in this study demonstrated its capacity to be used in designing rib support requirements and understanding coal mass and support mechanisms. [ABSTRACT FROM AUTHOR]

Process variants usually exhibit a high degree of internal heterogeneity, in the sense that the executions of the process differ widely from each other due to contextual factors, human factors, or deliberate business decisions. Understanding differences among process variants helps analysts and managers to make informed decisions as to how to standardise or otherwise improve a business process. Existing process variant mining approaches typically fall short in full supporting semantic process variability mining, especially rarely taking activity behaviour relationships and trace context semantic into consideration. Here, we propose a semantic process variant discovery method, aimed at solving the difficulty of distinguishing similar-but-different behaviours directly from event logs. More specifically, we adapt concepts of benchmark logs and trace context tree to formalise context semantic of event log, to classify benchmark logs into several parts, thereby the clustered trace cohorts are mapped to discover the configurable process variants. In the experimental part, some performance metrics of the proposed method are evaluated and calculated by real-world event logs, supporting the usefulness of the proposed method. The experimental results show that the proposed method is able to distinguish similar-but-different behaviours and is superior to the characteristic trace clustering method using conventional neural networks. [ABSTRACT FROM AUTHOR]

The problem of hydraulic parameters estimation lies in the depth: the deeper the rock formation, the more expensive and difficult the field tests and samples acquisition, and the more challenging the technical issues. The article assesses the Triassic sandstone's drainage potential at the stage of shaft sinking. It focuses on parameter analysis in varied scales, from drill-core sample laboratory testing, through a single well drawing test, to long-term pumping and recovery tests in the well with observation piezometers. The obtained results are compared to the values estimated in the past using different methods. Finally, the paper states whether it is reliable to forecast pore-fissure sandstone drainage potential based only on core samples' laboratory tests. This research proved that lab tests underestimate pore-fissure rocks' hydraulic parameters (mean hydraulic conductivity k = 9.79 × 10 − 8 m/s) tenfold more than long-term pumping tests (mean k = 4.45 × 10 − 7 m/s). However, it can be concluded that the group of so-called "witness samples", 10% of all core samples with a top value of the hydraulic conductivity tested in the laboratory, can be representative of the aquifer and comparable to the values obtained in pumping tests. With this in mind, we recommend using the highest values of hydrogeological parameters from laboratory tests based on the worst-case scenario. Therefore, it is possible to forecast inflows to the shafts reliably. This methodology is recommended only for rocks of porous and pore-fissure character. [ABSTRACT FROM AUTHOR]

During the drilling process, horizontal drill strings are often subject to stick-slip and back-pressure phenomena, which undermine drilling safety and affect drilling costs. However, most of the models proposed in the existing literature apply only to the analysis of the drill string motion state and are irrelevant to the rotary drilling process in coal mines. This paper presents a lumped-mass model of a drill string in a horizontal coal mine well. First, based on reasonable assumptions, the drill-string system was considered as four lumped-mass units. The lateral-torsional dynamics model was established based on the Lagrange dynamics theory. Further, combined with lateral movement, the intermittent contact between the drill string and borehole wall is described, and the friction torque due to this intermittent contact is calculated. Combined with the Karnopp bit-rock interaction, a drill-string dynamics model of horizontal wells that considers intermittent contact friction was developed. The proposed horizontal well drill-string model is more suitable for rotary drilling in a coal mine, and the nonlinear influencing factors are considered more comprehensive. [ABSTRACT FROM AUTHOR]

Recently, The Egyptian health sector whether it is public or private; utilizes emerging technologies such as data mining, business intelligence, Internet of Things (IoT), among many others to enhance the service and to deal with increasing costs and growing pressures. However, process mining has not yet been used in the Egyptian organizations, whereas the process mining can enable the domain experts in many fields to achieve a realistic view of the problems that are currently happening in the undertaken field, and thus solve it. This paper presents application of the process mining techniques in the healthcare field to obtain meaningful insights about its careflows, e.g., to discover typical paths followed by certain patient groups. Also, to analyze careflows that have a high degree of dynamic and complexity. The proposed methodology starts by the preprocess step on the event logs to eliminate outliers and clean the event log. And then apply a set of the popular discovery miner algorithms to discover the process model. Then careflows processes are analyzed from three main perspectives: the control-flow perspective, the performance perspective and, the organizational perspective. That contributes with many insights for the domain experts to improve the existing careflows. Through evaluating the simplicity metric of extracted models; the paper suggested a method to quantify the simplicity metric. The paper used a dataset from a cardiac surgery unit in an Egyptian hospital. The results of the applied process mining techniques provide the hospital managers a real analysis and insights to make the patient journey easier. [ABSTRACT FROM AUTHOR]

Background: Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease whose spreading and progression mechanisms are still unclear. The ability to predict ALS prognosis would improve the patients' quality of life and support clinicians in planning treatments. In this paper, we investigate ALS evolution trajectories using Process Mining (PM) techniques enriched to both easily mine processes and automatically reveal how the pathways differentiate according to patients' characteristics. Methods: We consider data collected in two distinct data sources, namely the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) dataset and a real-world clinical register (ALS–BS) including data of patients followed up in two tertiary clinical centers of Brescia (Italy). With a focus on the functional abilities progressively impaired as the disease progresses, we use two Process Discovery methods, namely the Directly-Follows Graph and the CareFlow Miner, to mine the population disease trajectories on the PRO-ACT dataset. We characterize the impairment trajectories in terms of patterns, timing, and probabilities, and investigate the effect of some patients' characteristics at onset on the followed paths. Finally, we perform a comparative study of the impairment trajectories mined in PRO-ACT versus ALS–BS. Results: We delineate the progression pathways on PRO-ACT, identifying the predominant disabilities at different stages of the disease: for instance, 85% of patients enter the trials without disabilities, and 48% of them experience the impairment of Walking/Self-care abilities first. We then test how a spinal onset increases the risk of experiencing the loss of Walking/Self-care ability as first impairment (52% vs. 27% of patients develop it as the first impairment in the spinal vs. the bulbar cohorts, respectively), as well as how an older age at onset corresponds to a more rapid progression to death. When compared, the PRO-ACT and the ALS–BS patient populations present some similarities in terms of natural progression of the disease, as well as some differences in terms of observed trajectories plausibly due to the trial scheduling and recruitment criteria. Conclusions: We exploited PM to provide an overview of the evolution scenarios of an ALS trial population and to preliminary compare it to the progression observed in a clinical cohort. Future work will focus on further improving the understanding of the disease progression mechanisms, by including additional real-world subjects as well as by extending the set of events considered in the impairment trajectories. [ABSTRACT FROM AUTHOR]

Object-centric process mining is a novel branch of process mining that aims to analyze event data from mainstream information systems (such as SAP) more naturally, without being forced to form mutually exclusive groups of events with the specification of a case notion. The development of object-centric process mining is related to exploiting object-centric event logs, which includes exploring and filtering the behavior contained in the logs and constructing process models which can encode the behavior of different classes of objects and their interactions (which can be discovered from object-centric event logs). This paper aims to provide a broad look at the exploration and processing of object-centric event logs to discover information related to the lifecycle of the different objects composing the event log. Also, comprehensive tool support (OC-PM) implementing the proposed techniques is described in the paper. [ABSTRACT FROM AUTHOR]

During the in situ leaching process of ion-adsorption rare earths, leaching solution needs to be constantly injected to the mine slopes. As a consequence, landslides are highly likely to occur due to the increasing water level of soil mass. To solve this problem, we conducted a mechanical analysis on the rising water level after solution injection, which shed light on the mechanical principle of slope instability brought about by rising water level. With water level variation as the major factor, we established an early warning system for landslide risks on the basis of the real-time monitoring of water level. Within the system, a self-designed landslide early warning model is embedded. In addition to monitoring the water level variation in slopes, this system can be employed for real-time data processing. With the integration of early warning model algorithm, the real-time graded early warning of slope landslide risks is achieved within the mining process of ion-adsorption rare earths. By discussing the real-time monitoring method, framework of landslide early warning system, FIFC landslide early warning model, optimization method of water level, and selection of landslide-inducing factors, this research provides an effective solution to the landslide early warning within the mining process of ion-adsorption rare earth minerals. Thus, it can be employed as a favorable reference for other types of early warning systems. [ABSTRACT FROM AUTHOR]

Human-Computer Interaction (HCI) research has extensively employed eye-tracking technologies in a variety of fields. Meanwhile, the ongoing development of Internet Protocol TV (IPTV) has significantly enriched the TV customer experience, which is of great interest to researchers across academia and industry. A previous study was carried out at the BT Ireland Innovation Centre (BTIIC), where an eye tracker was employed to record user interactions with a Video-on-Demand (VoD) application, the BT Player. This paper is a complementary and subsequent study of the analysis of eye-tracking data in our previously published introductory paper. Here, we propose a method for integrating layout information from the BT Player with mining the process of customer eye movement on the screen, thereby generating HCI and Industry-relevant insights regarding user experience. We incorporate a popular Machine Learning model, a discrete-time Markov Chain (DTMC), into our methodology, as the eye tracker records each gaze movement at a particular frequency, which is a good example of discrete-time sequences. The Markov Model is found suitable for our study, and it helps to reveal characteristics of the gaze movement as well as the user interface (UI) design on the VoD application by interpreting transition matrices, first passage time, proposed 'most likely trajectory' and other Markov properties of the model. Additionally, the study has revealed numerous promising areas for future research. And the code involved in this study is open access on GitHub. [ABSTRACT FROM AUTHOR]

Despite the importance of small and medium-sized enterprises (SMEs) for the growth and development of companies, the high failure rate of these companies persists, and this correspondingly demands the attention of managers. Thus, to boost the company success rate, we may deploy certain approaches, for example predictive models, specifically for the SME innovation. This study aims to examine the variables that positively shape and contribute towards innovation of SMEs. Based on the Spinner innovation model, we explore how to predict the innovation of SMEs by applying the variables, namely knowledge creation, knowledge transfer, public knowledge management, private knowledge management and innovation. This study applied the data mining technique according to the cross industry standard process for data mining (CRISP-DM) method while the Statistical Package for the Social Sciences (SPSS_Version28) served to analyze the data collected from 208 SME employees in Oporto, Portugal. The results demonstrate how the Spinner innovation model positively influences the contributions of the SMEs. This SME-dedicated model fosters the creation of knowledge between internal and external interactions and increases the capacity to predict the SME innovation by 56%. [ABSTRACT FROM AUTHOR]

Background and objective: Recently, Electronic Health Records (EHR) are increasingly being converted to Common Data Models (CDMs), a database schema designed to provide standardized vocabularies to facilitate collaborative observational research. To date, however, rare attempts exist to leverage CDM data for healthcare process mining, a technique to derive process-related knowledge (e.g., process model) from event logs. This paper presents a method to extract, construct, and analyze event logs from the Observational Medical Outcomes Partnership (OMOP) CDM for process mining and demonstrates CDM-based healthcare process mining with several real-life study cases while answering frequently posed questions in process mining, in the CDM environment. Methods: We propose a method to extract, construct, and analyze event logs from the OMOP CDM for process types including inpatient, outpatient, emergency room processes, and patient journey. Using the proposed method, we extract the retrospective data of several surgical procedure cases (i.e., Total Laparoscopic Hysterectomy (TLH), Total Hip Replacement (THR), Coronary Bypass (CB), Transcatheter Aortic Valve Implantation (TAVI), Pancreaticoduodenectomy (PD)) from the CDM of a Korean tertiary hospital. Patient data are extracted for each of the operations and analyzed using several process mining techniques. Results: Using process mining, the clinical pathways, outpatient process models, emergency room process models, and patient journeys are demonstrated using the extracted logs. The result shows CDM's usability as a novel and valuable data source for healthcare process analysis, yet with a few considerations. We found that CDM should be complemented by different internal and external data sources to address the administrative and operational aspects of healthcare processes, particularly for outpatient and ER process analyses. Conclusion: To the best of our knowledge, we are the first to exploit CDM for healthcare process mining. Specifically, we provide a step-by-step guidance by demonstrating process analysis from locating relevant CDM tables to visualizing results using process mining tools. The proposed method can be widely applicable across different institutions. This work can contribute to bringing a process mining perspective to the existing CDM users in the changing Hospital Information Systems (HIS) environment and also to facilitating CDM-based studies in the process mining research community. [ABSTRACT FROM AUTHOR]

Given the increasing availability of electronic health records, it has become increasingly feasible to adopt data-driven approaches to capture a deep understanding of the patient journeys. Nevertheless, simply using data-driven techniques to depict the patient journeys without an integrated modeling and analysis approach is proving to be of little benefit for improving patients' experiences. Indeed, a model of the journey patterns is necessary to support the improvement process. We presented a 3-phase methodology that integrates a process mining–based understanding of patient journeys with a stochastic graphical modeling approach to derive and analyze the analytical expressions of some important performance indicators of an emergency department including mean and variance of patients' length of stay (LOS). Analytical expressions were derived and discussed for mean and variance of LOS times and discharge and admission probabilities. LOS differed significantly depending on whether a patient was admitted to the hospital or discharged. Moreover, multiparameter sensitivity equations are obtained to identify which activities contribute the most in reducing the LOS at given operating conditions so decision makers can prioritize their improvement initiatives. Data-driven based approaches for understanding the patient journeys coupled with appropriate modeling techniques yield a promising tool to support improving patients' experiences. The modeling techniques should be easy to implement and not only should be capable of deriving some key performance indicators of interest but also guide decision makers in their improvement initiatives. • Extracting patient journeys patterns factually and objectively from occurrences of processes and activities is arguably one of the most important aspects of analyzing patient journeys. • This study describes the use of process mining as a tool to obtain a deep understanding of executed care processes from electronic health records. • Process mining–based patient journey patterns are used to derive and discuss patients' length of stay, as a crucial piece of patient experiences and of particular importance for measuring operational performance of hospitals. [ABSTRACT FROM AUTHOR]

Background: Currently, many central auditory processing disorder screening tests are available for children, and serious games (SGs) are frequently used as a tool for the diagnosis of different neural deficits and disorders in health care. However, it has not been possible to find a proposal that unifies both ideas. In addition, the validation and improvement of SGs, in general, does not take into account the player-game interaction, thus omitting valuable information about the playability and usability of the game. Objective: This study presented Amalia's Planet, a game conceived for use in school environments, which allows a first assessment of a child through their performance of the proposed tasks related to different aspects of auditory performance. In addition, the game defines a series of events in relation to the execution of the tasks, which were evaluated for the subsequent optimization of its performance and the improvement of its usability. Methods: Using screening tools based on the use of SG technologies, a total of 87 school-age children were evaluated to test the various hypotheses proposed in this study. By grouping users according to whether they had personal history of hearing pathologies, the discriminant power, playability, and usability of the final solution were examined using traditional statistical techniques and process mining (PM) algorithms. Results: With a confidence level of 80% for test 2 (P=.19), there was no statistical evidence to reject the null hypothesis that a player's performance is affected by whether the player had a previous auditory pathology. Furthermore, the tool allowed the screening of 2 players initially categorized as healthy because of their low level of performance in the tests and the similarity of their behavior with that of the group of children with a previous pathology. With regard to the validation of the proposed solution, the use of PM techniques made it possible to detect the existence of events that lasted too long, which can lead to player frustration, and to discover small structural flaws in the game. Conclusions: SGs seem to be an appropriate tool for the screening of children at risk of central auditory processing disorder. Moreover, the set of PM techniques provides a reliable source of information regarding the playability and usability of the solution to the development team, allowing its continuous optimization. [ABSTRACT FROM AUTHOR]

Within process mining, discovery techniques make it possible to construct business process models automatically from event logs. However, results often do not achieve a balance between model complexity and fitting accuracy, establishing a need for manual model adjusting. This paper presents an approach to process mining that provides semi-automatic support to model optimization based on the combined assessment of model complexity and fitness. To balance complexity and fitness, a model simplification approach is proposed, which abstracts the raw model at the desired granularity. Additionally, we introduce a concept of meta-states, a cycle collapsing in the model, which can potentially simplify the model and interpret it. We aim to demonstrate the capabilities of our technological solution using three datasets from different applications in the healthcare domain. These are remote monitoring processes for patients with arterial hypertension and workflows of healthcare workers during the COVID-19 pandemic. A case study also investigates the use of various complexity measures and different ways of solution application, providing insights on better practices in improving interpretability and complexity/fitness balance in process models. [ABSTRACT FROM AUTHOR]

With the arrival of the current digital era and the advancement of information transmission technologies, there has been an unprecedented rise in data. Efficient extraction of useful information from the volumes of data has garnered growing interest from academics and the industry. Data mining research focuses on finding utility patterns in large datasets. But the inherent complications like frequent scans, creation of substantial candidate sets, etc. plague the mining process for large datasets. Distributive architecture-based approaches also prove inefficacious due to high communication overhead over iterations. High communication cost over data exchange both locally and remotely further aggravates the situation. We propose a Communication Cost Effective Utility-based Pattern Mining (CEUPM) algorithm based on the Spark framework to address this issue. Spark accelerates iterative scanning by storing scanned datasets in a memory abstraction called resilient distributed datasets (RDD). RDD operations need a redistribution of data among cluster nodes during processing. To minimize the communication cost incurred during the shuffle process, we adopt a search space division strategy based on data parallelism for a fair and effective task allocation across cluster nodes. Communication overhead is incurred during this redistribution or shuffle process while minimizing costs. Experimental results in four real datasets demonstrate that CEUPM considerably reduces shuffling overhead and outperforms other existing methods in terms of memory usage, communication cost, execution time, and scalability. [ABSTRACT FROM AUTHOR]

Business processes are omnipresent in nowadays economy: companies operate repetitively to achieve their goals, e.g., deliver goods, complete orders. The business process model is the key to understanding, managing, controlling, and verifying the operations of a company. Modeling of business processes may be a legal requirement in some market segments, e.g., financial in the European Union, and a prerequisite for certification, e.g., of the ISO-9001 standard. However, business processes naturally evolve, and continuous model adaptation is essential for rapid spot and reaction to changes in the process. The main contribution of this work is the Continuous Inductive Miner (CIM) algorithm that discovers and continuously adapts the process tree, an established representation of the process model, using the batches of event logs of the business process. CIM joins the exclusive guarantees of its two batch predecessors, the Inductive Miner (IM) and the Inductive Miner – directly-follows-based (IMd): perfectly fit and sound models, and single-pass event log processing, respectively. CIM offers much shorter computation times in the update scenario than IM and IMd. CIM employs statistical information to work around the need to remember event logs as IM does while ensuring the perfect fit, contrary to IMd. [ABSTRACT FROM AUTHOR]

Taking hydraulic punching test of Zhaozhuang Mine as engineering background, by comparing the field application effects of different hydraulic punching processes, the relationship between coal moisture content around the borehole blockage and gas extraction effect was monitored and analyzed. The following conclusions were drawn: the occurrence of borehole blockage was the result of the comprehensive action of coal slag particle size and slag discharge capacity of punching return water. Blocking the borehole led to the accumulation of a large amount of punching high-pressure water and coal slag in the borehole, which compressed and infiltrated the surrounding coal body and reduced gas extraction efficiency of borehole. Under the condition of the same amount of coal produced by punching, the different punching methods led to the great difference in the influence radius of punching. The large flow three-stage coal-breaking hydraulic punching system developed according to the borehole blockage mechanism can effectively reduce the particle size of coal slag, increase the punching flow, effectively avoid borehole blockage, and improve gas extraction efficiency of borehole. [ABSTRACT FROM AUTHOR]

Concept drift in processmining (PM) is a challenge as classicalmethods assume processes are in a steady-state, i.e., events share the same process version. We conducted a systematic literature review on the intersection of these areas, and thus, we review concept drift in PM and bring forward a taxonomy of existing techniques for drift detection and online PM for evolving environments. Existing works depict that (i) PM still primarily focuses on oline analysis, and (ii) the assessment of concept drift techniques in processes is cumbersome due to the lack of common evaluation protocol, datasets, and metrics. [ABSTRACT FROM AUTHOR]

In recent years, the mining of cryptocurrencies such as Bitcoin has attracted much global attention due to the simplicity and high profitability of this business, especially in countries with low electricity rates. The process of cryptocurrency mining using cryptocurrency mining devices requires a significant amount of computing power. This industry, which is increasingly growing in energy and carbon emissions, can cause environmental concerns. Common ways to reduce this amount of energy consumption are to prohibit mining and charge these loads at a higher electricity rate; however, these solution methods are not feasible and applicable in many countries. This paper presents an energy efficiency program to persuade the customers to mine cryptocurrency using high‐efficient cryptocurrency mining devices (CMDs), including informative, regulation, and financial policy instruments. This energy efficiency approach suggests a standard, if implemented, would ban the application of inefficient CMDs, and would encourage the cryptocurrency miners to improve the profitability of their business with high‐efficient CMDs through a penalty‐reward strategy. The performance of this energy efficiency program is studied through an analysis of data gathered from different CMDs used in Iran. The effectiveness of this suggested program is proved through its sufficient incentives for miners using high‐efficient CMDs. [ABSTRACT FROM AUTHOR]